Research on medical data classification based on incremental learning in hybrid RBF-ELM Network
To improve the data classification performance of traditional learning algorithms,a hybrid RBF-ELM network(IHRBF-ELM)based on incremental learning is proposed and applied to medical data classification problcems.In the implementation of network structure,the RBF hidden layer is cascaded with the ELM,which means an RBF mapping layer is added between the connecting input layer and the ELM hidden layer;in the implementation of learning algorithms,the incremental learning algorithm based on potential function clustering is first used to automatically optimize and estimate the number of Gaussian kernels and kernel parameters in the RBF hidden layer,and then the extreme learning machine optimization algorithm is utilized to optimize the network output weights.The algorithm was experimentally compared with PFRBF,ELM,HRBF-BP algorithms on different medical classification datasets,and the results showed that the network classification accuracy of IHRBF-ELM algorithm is higher.Thus,the method proposed has good reference value for improving the data classification ability of traditional learning algorithms.
万方数据
